Reinforcement Learning Decision and Planning Algorithm Using Posterior Return Heuristic

The complexities and dynamics of modern driving environments have amplified the uncertainties faced by autonomous driving systems, posing significant challenges to their decision-making processes. This paper introduces a novel reinforcement learning framework inspired by posterior return estimation, designed to enhance safety and foresight in robotic decision-making. To overcome the challenges of long-term dependency in reinforcement learning, a tree structure is utilized to record the interaction history between the agent and the environment. The posterior distribution of returns for each state is estimated from this historical data, with a neural network employed to model this distribution for enhanced decision-making. The framework's efficacy is validated through two decision-making and planning scenarios, where it surpasses several commonly used frameworks and demonstrates notably more anticipatory decision-making patterns.